scribe-distill

Convert confirmed security findings into automated detection modules with YAML frontmatter.

63|8|Updated Feb 16, 2026
One-click install
npx skills add https://github.com/JoranHonig/grimoire --skill scribe-distill
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: scribe-distill
Source: https://github.com/JoranHonig/grimoire/tree/main/skills/scribe-distill
Command: npx skills add https://github.com/JoranHonig/grimoire --skill scribe-distill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The scribe-distill skill converts confirmed security findings into repeatable, discoverable detection artifacts so researchers do not lose remediation knowledge and can automatically scan for recurring issues.

Core Features & Use Cases

  • Pattern extraction and generalization: Strip instance-specific details to express the vulnerability as a reusable code shape or invariant.
  • Feasibility assessment: Classify findings as static-feasible, agentic-feasible, or not automatable using the provided criteria.
  • Sigil creation and validation: Produce checks or knowledge artifacts with YAML frontmatter, grep-able patterns, assessment guidance, and validation steps to integrate into the project's spellbook.
  • Use case: After confirming a vulnerability in an audit, run scribe-distill to generate a detection check that can be applied across the codebase to find variants and prevent regressions.

Quick Start

Ask the scribe-distill skill to "distill this finding" and attach the finding file plus contextual metadata so it can assess feasibility and create a detection module.

Frequently Asked Questions about scribe-distill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I turn confirmed security findings into automated detection checks?

To turn security findings into automated detection checks, you distill the confirmed vulnerability into a reusable code shape. The process generates a detection module with YAML frontmatter, grep-able patterns, and validation steps to integrate into your project's spellbook.

What is the best way to automate vulnerability detection for recurring codebase issues?

Automating vulnerability detection involves transforming audit findings into discoverable knowledge artifacts. By extracting the vulnerability pattern and classifying its feasibility, you create reusable detection modules that automatically scan codebases to prevent regressions.

Can I classify whether a security finding is statically or agentically automatable?

Yes, you can assess the feasibility of automating a security finding. The distillation process classifies findings as static-feasible, agentic-feasible, or not automatable based on provided criteria to determine if automated detection checks can be generated.

How do I generalize a specific vulnerability instance into a reusable detection pattern?

Generalizing a vulnerability instance requires stripping instance-specific details to express the issue as a reusable code shape or invariant. This pattern extraction allows the specific finding to be converted into a grep-able detection check for broader codebase application.

Do I need to provide contextual metadata to generate a detection module from an audit finding?

Yes, you need to attach the finding file and contextual metadata to generate a detection module. This information is required to accurately assess the vulnerability's feasibility and create a validated check with proper assessment guidance.